Research design and data sources
A retrospective study was conducted using renal biopsy data from two hospitals, Shenzhen Second People's Hospital and Shenzhen People's Hospital, both located in areas with a population of over 1.5 million. Patients who underwent renal biopsy at the aforementioned hospitals between January 2016 and May 2023 were included, with the following inclusion criteria: 1) first-time kidney puncture patients; 2) complete clinical and pathological data. Exclusion criteria were as follows: 1) patients lacking a renal pathological diagnosis; 2) patients who underwent repeated renal biopsies. Various glomerular diseases were statistically analyzed based on their respective pathological diagnoses to compare their incidence before (1 January 2016 to 10 March 2020) and during (11 March 2020 to 5 May 2023) the COVID-19 pandemic (declared a global pandemic by the World Health Organization on 11 March 2020). These diseases included minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), IgA nephropathy (IgAN), lupus nephritis (LN), and diabetic nephropathy (DN). The focus was on studying the changes in the incidence rates of membranous nephropathy.
We used publicly available data on idiopathic membranous nephropathy (IMN) and COVID-19 infection for this study, which was from European and East Asian ancestors and published from 2020 to 202113,14. The dataset for European IMN comprised 2150 cases and 5829 controls from five European cohorts. Similarly, the dataset for East Asian IMN included 1632 cases and 3209 controls from four East Asian cohorts, excluding secondary causes of IMN such as systemic lupus erythematosus, hepatitis B virus infection, and carcinoma. The dataset for COVID-19 encompassed genetic determinants associated with susceptibility, severity, severe infection, critical illness, and prognosis, with sample sizes ranging from 1,058,410 to 1,887,658 for individuals of European ancestry and from 203 to 1,422 for individuals of East Asian ancestry. We focused on common, hospital-required, severe respiratory infections of COVID-19 as the outcome of interest.
Pathological analysis and categorization
Renal biopsy specimens were examined using light, immunofluorescence and electron microscopy. Specimens were stained with HE, PAS, PASM, Masson's trichrome, and Congo red for patients with kidney damage caused by specific components. The pathological classification follows the World Health Organization's (WHO) histological classification scheme for glomerular diseases in 195515,16. If a patient has two or more types of kidney disease, each type is classified separately.
Bi-Mendelian randomization and sensitivity analysis
In this study, we employed a bidirectional Mendelian randomization analysis using GWAS data from some research to investigate the potential causal association between COVID-19 and MN13,17 (refer to the supplementary document). We considered MN and common, hospital-required, severe respiratory infections of COVID-19 as the exposure and outcome variables, respectively. Instrumental variables satisfying the three key assumptions (refer to the supplementary document) were extracted and subjected to Mendelian randomization. The primary method for Mendelian randomization was Inverse Variance Weighting (IVW), Wald ratio, MR-Egger, Weighted mode, and Weighted median methods. Sensitivity analyses were conducted using IVW and MR-Egger18,19. To account for the possibility of reverse causality, we performed a reverse MR (swapping the exposure and outcome variables) and a Steiger filtering test for directional examination20.
Statistical analysis
All analyses were conducted using Empower (R) (http://www.empowerstats.com, X and Y solutions, Inc, Boston, MA, USA) and R (http://www.R-project.org). Figdraw (http://www.figdraw.com) and OriginLab (https://www.originlab.com) was used for the graphical plotting in this study. Descriptive statistics summarized the demographic and clinical characteristics of the study population. Continuous variables were expressed as mean ± standard deviation (SD) or median with interquartile range (IQR), while categorical variables were presented as counts and percentages. The Kolmogorov-Smirnov test was used to assess the normality of the continuous data distribution. The Mann-Whitney U test was employed to compare non-normally distributed continuous variables between the two groups, while the χ2 test or Fisher's exact test was used to compare categorical variables. Glomerular disease groups were compared to analyze changes in incidence rates before and after the COVID-19 pandemic, utilizing analysis of variance (ANOVA) and Bonferroni correction for multiple comparisons. P values < 0.05 (two- sided) were considered statistically significant.